{"success":true,"database":"eegdash","data":{"_id":"69d16e06897a7725c66f4ce6","dataset_id":"nm000348","associated_paper_doi":null,"authors":["Banghua Yang","Fenqi Rong","Yunlong Xie","Du Li","Jiayang Zhang","Fu Li","Guangming Shi","Xiaorong Gao"],"bids_version":"1.9.0","contact_info":null,"contributing_labs":null,"data_processed":false,"dataset_doi":"10.82901/nemar.nm000348","datatypes":["eeg"],"demographics":{"subjects_count":51,"ages":[29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29],"age_min":29,"age_max":29,"age_mean":29.0,"species":null,"sex_distribution":{"f":45,"m":6},"handedness_distribution":{"r":51}},"experimental_modalities":null,"external_links":{"source_url":"https://nemar.org/dataexplorer/detail/nm000348","osf_url":null,"github_url":null,"paper_url":null},"funding":[],"ingestion_fingerprint":"86871ae40679eee4d9eabc71a84d6760952315e61b37e33971d29256ab49cc91","license":"CC-BY-4.0","n_contributing_labs":null,"name":"Yang et al. 2025 — A multi-day and high-quality EEG dataset for motor imagery brain-computer interface","readme":"[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000348-blue)](https://doi.org/10.82901/nemar.nm000348)\nYang2025\n========\nMulti-day MI-BCI dataset (WBCIC-SHU) from Yang et al 2025.\nDataset Overview\n----------------\n  Code: Yang2025\n  Paradigm: imagery\n  DOI: 10.1038/s41597-025-04826-y\n  Subjects: 51\n  Sessions per subject: 3\n  Events: left_hand=1, right_hand=2\n  Trial interval: [1.5, 5.5] s\n  File format: BDF\nAcquisition\n-----------\n  Sampling rate: 1000.0 Hz\n  Number of channels: 59\n  Channel types: eeg=59, ecg=1, eog=4\n  Channel names: Fpz, Fp1, Fp2, AF3, AF4, AF7, AF8, Fz, F1, F2, F3, F4, F5, F6, F7, F8, FCz, FC1, FC2, FC3, FC4, FC5, FC6, FT7, FT8, Cz, C1, C2, C3, C4, C5, C6, T7, T8, CP1, CP2, CP3, CP4, CP5, CP6, TP7, TP8, Pz, P3, P4, P5, P6, P7, P8, POz, PO3, PO4, PO5, PO6, PO7, PO8, Oz, O1, O2\n  Montage: standard_1005\n  Hardware: Neuracle NeuSen W\n  Sensor type: Ag/AgCl\n  Line frequency: 50.0 Hz\n  Online filters: {}\nParticipants\n------------\n  Number of subjects: 51\n  Health status: healthy\n  Age: min=17.0, max=30.0\n  Gender distribution: female=18, male=44\n  Handedness: right-handed\n  BCI experience: naive\n  Species: human\nExperimental Protocol\n---------------------\n  Paradigm: imagery\n  Number of classes: 2\n  Class labels: left_hand, right_hand\n  Trial duration: 7.5 s\n  Study design: Multi-day MI-BCI: 2C (left/right hand, 51 subj) and 3C (left hand, right hand, foot-hooking, 11 subj). 3 sessions per subject on different days.\n  Feedback type: none\n  Stimulus type: video cues\n  Stimulus modalities: visual, auditory\n  Primary modality: visual\n  Synchronicity: synchronous\n  Mode: offline\nHED Event Annotations\n---------------------\n  Schema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser\n  left_hand\n    ├─ Sensory-event, Experimental-stimulus, Visual-presentation\n    └─ Agent-action\n       └─ Imagine\n          ├─ Move\n          └─ Left, Hand\n  right_hand\n    ├─ Sensory-event, Experimental-stimulus, Visual-presentation\n    └─ Agent-action\n       └─ Imagine\n          ├─ Move\n          └─ Right, Hand\nParadigm-Specific Parameters\n----------------------------\n  Detected paradigm: motor_imagery\n  Imagery tasks: left_hand, right_hand, feet\n  Cue duration: 1.5 s\n  Imagery duration: 4.0 s\nData Structure\n--------------\n  Trials: 39600\n  Trials context: 51 subjects x 3 sessions x 200 trials (2C) + 11 subjects x 3 sessions x 300 trials (3C) = 39600\nSignal Processing\n-----------------\n  Classifiers: CSP+SVM, FBCSP+SVM, EEGNet, deepConvNet, FBCNet\n  Feature extraction: CSP, FBCSP\n  Frequency bands: bandpass=[0.5, 40.0] Hz\n  Spatial filters: CSP, FBCSP\nCross-Validation\n----------------\n  Method: 10-fold\n  Folds: 10\n  Evaluation type: within_session\nBCI Application\n---------------\n  Applications: motor_control\n  Environment: laboratory\n  Online feedback: False\nTags\n----\n  Pathology: Healthy\n  Modality: Motor\n  Type: Research\nDocumentation\n-------------\n  DOI: 10.1038/s41597-025-04826-y\n  License: CC-BY-4.0\n  Investigators: Banghua Yang, Fenqi Rong, Yunlong Xie, Du Li, Jiayang Zhang, Fu Li, Guangming Shi, Xiaorong Gao\n  Institution: Shanghai University\n  Country: CN\n  Data URL: https://plus.figshare.com/articles/dataset/22671172\n  Publication year: 2025\nReferences\n----------\nYang, B., Rong, F., Xie, Y., et al. (2025). A multi-day and high-quality EEG dataset for motor imagery brain-computer interface. Scientific Data, 12, 488. https://doi.org/10.1038/s41597-025-04826-y\nAppelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Hochenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896). https://doi.org/10.21105/joss.01896\nPernet, C. R., Appelhoff, S., Gorgolewski, K. J., Flandin, G., Phillips, C., Delorme, A., Oostenveld, R. (2019). EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. Scientific Data, 6, 103. https://doi.org/10.1038/s41597-019-0104-8\n---\nGenerated by MOABB 1.5.0 (Mother of All BCI Benchmarks)\nhttps://github.com/NeuroTechX/moabb","recording_modality":["eeg"],"senior_author":"Xiaorong Gao","sessions":["0","1","2"],"size_bytes":165139463639,"source":"nemar","storage":{"backend":"nemar","base":"s3://nemar/nm000348","raw_key":"dataset_description.json","dep_keys":["CHANGES","README.md","participants.json","participants.tsv"]},"study_design":null,"study_domain":null,"tasks":["imagery"],"timestamps":{"digested_at":"2026-10-22T09:00:28.120741+00:00","dataset_created_at":null,"dataset_modified_at":"2026-08-18 22:51:54"},"total_files":459,"computed_title":"Yang et al. 2025 — A multi-day and high-quality EEG dataset for motor imagery brain-computer interface","nchans_counts":[{"val":64,"count":153}],"sfreq_counts":[{"val":1000.0,"count":153}],"stats_computed_at":"2026-10-22T09:54:07.650196+00:00","total_duration_s":354334.0,"tagger_meta":{"model":"gpt-6-sol","taxonomy":"v2","config_hash":"39931253008fe2df","tagged_at":"2026-10-06T13:13:11Z","source":"eegdash-llm-tagger"},"tags":{"pathology":["Healthy"],"modality":["Multisensory"],"type":["Motor"],"confidence":{"pathology":0.8,"modality":0.7,"type":0.9},"reasoning":{"few_shot_analysis":"The EEG Motor Movement/Imagery Dataset is the closest paradigm match: visually cued hand movement imagery is labeled Healthy, Visual, and Motor. It supports Motor as the research type and shows that a cue, rather than the imagined movement, determines stimulus modality. The Parkinson's cross-modal oddball example shows the convention for labeling a task that uses both visual and auditory stimuli as Multisensory.","metadata_analysis":"The readme states \"Health status: healthy\" and \"Pathology: Healthy.\" It identifies the \"Detected paradigm: motor_imagery\" with \"Class labels: left_hand, right_hand.\" For stimuli, it says \"Stimulus modalities: visual, auditory\" but also \"Primary modality: visual\" and describes \"Stimulus type: video cues.\" The hand-event annotations specify \"Visual-presentation\" and \"Imagine\" followed by \"Move.\" The participants overview gives different sex and age summaries from the readme, but neither source identifies a clinical recruitment condition.","paper_abstract_analysis":"No useful paper information; a publication citation is provided, but no abstract.","evidence_alignment_check":"Pathology: Metadata says \"Health status: healthy\" and \"Pathology: Healthy\"; the healthy motor-imagery example suggests Healthy. ALIGN. Modality: Metadata says \"Stimulus modalities: visual, auditory,\" while \"Primary modality: visual\" and the event annotations emphasize visual cues. The motor-imagery example suggests Visual, whereas the cross-modal example suggests Multisensory when both channels are used. PARTIAL CONFLICT: the explicit two-channel metadata favors Multisensory over the visual-only analogy. Type: Metadata says \"Detected paradigm: motor_imagery\" and annotates imagined movement; the motor-imagery example suggests Motor. ALIGN.","decision_summary":"Pathology candidates: Healthy, supported by \"Health status: healthy\" and \"Pathology: Healthy,\" versus Unknown, for which there is no supporting evidence. Select Healthy (0.8; two explicit statements and a matching example). Modality candidates: Multisensory, supported by \"Stimulus modalities: visual, auditory,\" versus Visual, supported by \"Primary modality: visual,\" \"Stimulus type: video cues,\" and visual event annotations. Select Multisensory (0.7): the explicit listing of two stimulus channels governs, although auditory delivery is not described in detail. Type candidates: Motor, supported by \"Detected paradigm: motor_imagery,\" \"Class labels: left_hand, right_hand,\" and imagined-movement annotations, versus Other, with no specific supporting evidence. Select Motor (0.9; three explicit features and a close few-shot match). No metadata fact is overridden by a few-shot pattern."}},"canonical_name":null,"name_confidence":0.74,"name_meta":{"suggested_at":"2026-04-14T10:18:35.344Z","model":"openai/gpt-5.2 + openai/gpt-5.4-mini + deterministic_fallback"},"name_source":"author_year","author_year":"Yang2025","associated_paper_meta":{"channel":"text/readme","confidence":"high","author_overlap":6,"is_oa":true,"oa_status":"gold","source":"paper_resolver"},"generated_by":[{"CodeURL":"https://github.com/NeuroTechX/moabb","Name":"moabb","Description":"Mother of All BCI Benchmarks","Version":"1.5.0"}],"references_and_links":["https://plus.figshare.com/articles/dataset/22671172"],"source_datasets":[{"DOI":"10.1038/s41597-025-04826-y","URL":"https://plus.figshare.com/articles/dataset/22671172"},{"DOI":"doi:10.1038/s41597-025-04826-y"}],"bad_channels_info":null,"n_records":153}}